Detection of Intratumor Heterogeneity in Modern Pathology: A Multisite Tumor Sampling Perspective

Current sampling protocols of neoplasms along the digestive tract and in the urinary bladder have to be updated, as they do not respond to the necessities of modern personalized medicine. We show here that an adapted version of multisite tumor sampling (MSTS) is a sustainable model to overcome current deficiencies in digestive and bladder tumors when they are large enough so as to make unaffordable a total sampling. The new method is based on the divide-and-conquer algorithm and includes a slight modification of the MSTS, which proved to be useful very recently in clear cell renal cell carcinoma. This in silico analysis confirms the usefulness of MSTS for detecting intratumor heterogeneity (ITH) in tumors arising in hollow viscera. However, MSTS does not seem to improve routine traditional sampling in detecting tumor budding, extramural venous invasion, and perineural invasion. We conclude that (1) MSTS is the best method for tumor sampling to detect ITH balancing high performance and sustainable cost, (2) MSTS must be adapted to tumor shape and tumor location for an optimal performance.JC acknowledges financial support from Ikerbasque: The Basque Foundation for Science. This work was partially funded by grant SAF2013-48812-R from Ministerio de Economia y Competitividad (Spain) to JL; grant DPI2016-79874-R from Ministerio Economia y Competitividad (Spain) and FEDER to JC

Tree extraction and estimation of walnut structure parameters using airborne LiDAR data

[EN] The development of new tools based on remote sensing data in agriculture contributes to cost reduction, increased production, and greater profitability. Airborne LiDAR (Light Detection and Ranging) data show a significant potential for geometrically characterizing tree plantations. This study aims to develop a methodology to extract walnut (Juglans regia L.) crowns under leafless conditions using airborne LiDAR data. An original approach based on the alpha-shape algorithm, identification of local maxima, and k-means algorithms is developed to extract the crowns of walnut trees in a plot located in Viver (Eastern Spain) with 192 trees. In addition, stem diameter and volume, crown diameter, total height, and crown height were estimated from cloud metrics and other 2D parameters such as crown area, and diameter derived from LiDAR data. A correct identification was made of 178 trees (92.7%). For structure parameters, the most accurate results were obtained for crown diameter, stem diameter, and stem volume with coefficient of determination values (R-2) equal to 0.95, 0.87 and 0.83; and RMSE values of 0.43 m (5.70%), 0.02 m (9.35%) and 0.016 m(3) (21.55%), respectively. The models that gave the lowest R-2 values were 0.69 for total height and 0.70 for crown height, with RMSE values of 0.84 m (12.4%) and 0.83 m (14.5%), respectively. A suitable definition of the central and lower parts of tree canopies was observed. Results of this study generate valuable information, which can be applied for improving the management of walnut plantations.Estornell Cremades, J.; Hadas, E.; Marti-Gavila, J.; López- Cortés, I. (2021). Tree extraction and estimation of walnut structure parameters using airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation. 96:1-9. https://doi.org/10.1016/j.jag.2020.102273S199

Promoting sustainable consumption in Higher Education Institutions through integrative co-creative processes involving relevant stakeholders

The United Nations proposes to ensure a sustainable future for all through the Sustainable Development Goals, assigning a new role to each individual in all sectors of society. Higher Education Institutions are outstanding agents of change, introducing and implementing sustainability in a holistic way, connecting people, and including social and institutional considerations, with students being a key component of change. This study presents a co-creation model to incorporate sustainability in Higher Education Institutions, integrating all members of the university community with a multidisciplinary approach, seeking to address global needs with development tools for new products and services to facilitate the transition of consumers towards responsible consumption. The model aims to analyze the daily consumption pattern of the community at the university, to identify the degree of commitment to sustainability of its members, and to co-create in search of solutions related to responsible consumption and production. This is achieved through five phases of a model, each with specific tasks and objectives based on co-creation processes and tools. As a result, the model enables stakeholders to understand the needs of their community by actively participating within the five phases for developing more democratic solutions and social involvement regarding sustainability issues that can be solved through a co-creative process. The model combines the benefits through ethnographic techniques to discover habits, tools to involve participation, and co-creation to manage complex problems. Future research will focus on the application of the proposed model to more generalist contexts of society, addressing potential challenges due to vertical collaboration and barriers pre-established by society for the adoption of a sustainable lifestyle

Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM)

[EN] Remote sensing techniques are increasingly used for crop monitoring to improve the profitability of plantations. These studies are mainly based on spectral information recorded by satellites or unmanned aerial vehicles. However, the development of Earth Observation Systems capable of retrieving 3D point clouds at an affordable cost enables the possibility of exploring new approaches in agriculture. In this context, more research is required to analyze the capability of 3D data for inventory, management and prediction of inputs (water, fertilizers and pesticides) and outputs (production, biomass) of fruit plantations. To do this, the complete representation of each tree contribute to extract the main geometric parameters. The objective of this work is to obtain regression models to estimate total height (H-t), crown height (H-c), stem diameter (D-s), crown diameter (D-c), stem volume (V-s) and crown volume (V-c) from 45 walnut specimens. For this, 3D models were computed for these trees by applying ground-based Structure from Motion (SfM). A circular photogrammetric survey of each tree was carried out using a standard digital camera and three-dimensional point clouds were retrieved for each tree. From these data, the tree parameters were computed. Linear regression models were obtained to estimate H-t, H-c, D-s, D-c, V-s and V-c, with R-2 values between 0.89 and 0.99. The results showed accurate fits between field parameters and those derived from 3D point clouds retrieved from SfM technique, indicating the applicability of this cost-effective method to model walnut trees and to extract their accurate parameters without costly field campaigns.Fernández-Sarría, A.; López- Cortés, I.; Marti-Gavila, J.; Estornell Cremades, J. (2022). Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM). Journal of the Indian Society of Remote Sensing. 50(10):1931-1944. https://doi.org/10.1007/s12524-022-01576-x193119445010Arnó, J., Masip, J., & Rosell-Polo, J. R. (2015). Influence of the scanned side of the row in terrestrial laser sensor applications in vineyards: Practical consequences. Precision Agriculture, 16(2), 119–128. https://doi.org/10.1007/s11119-014-9364-7Arnó, J., Vallès, J. M., Llorens, J., Sanz, R., Masip, J., Palacín, J., & Rosell-Polo, J. R. (2013). Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precision Agriculture, 14(3), 290–306. https://doi.org/10.1007/s11119-012-9295-0Auat Cheein, F., Guivant, J., Sanz, R., Escolà, A., Yandún, F., Torres-Torriti, M., & Rosell-Polo, J. R. (2015). Real-time approaches for characterization of fully and partially scanned canopies in groves. COMPAG, 118, 361–371. https://doi.org/10.1016/j.compag.2015.09.017Bork, E. W., & Su, J. G. (2007). Integrating LiDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis. Remote Sensing of Environment, 111, 11–24. https://doi.org/10.1016/j.rse.2007.03.011Brede, B., Calders, K., Lau, A., Raumonen, P., Bartholomeus, H., Herold, M., & Kooistra, L. (2019). Non-destructivetree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR. Remote Sensing of Environment, 233, 111355. https://doi.org/10.1016/j.rse.2019.111355Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32, 1309–1332. https://doi.org/10.1109/tro.2016.2624754Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., Culvenor, D., Avitabile, V., Disney, M., Armston, J., & Kaasalainen, M. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6, 198–208. https://doi.org/10.1111/2041-210X.12301Chang, A., Jung, J., Maeda, M. M., & Landivar, J. (2017). Crop height monitoring with digital imagery from unmanned aerial system (UAS). COMPAG, 141, 232–237. https://doi.org/10.1016/j.compag.2017.07.008Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019Dalla, A., Rex, F., Almeida, D., Sanquetta, C., Silva, C., Moura, M., Wilkinson, B., et al. (2020). Measuring individual tree diameter and height using gatoreyehigh-density UAV-lidar in an integrated crop-livestock-forest system. Remote Sensing, 12, 863. https://doi.org/10.3390/rs12050863De Eugenio, A., Fernández-Landa, A., & Merino-de-Miguel, S. (2018). 3D models from terrestrial photogrammetry in the estimation of forest inventory variables. Revista de Teledetección, 51, 113–124. https://doi.org/10.4995/raet.2018.9174Díaz-Varela, R., de la Rosa, R., León, L., & Zarco-Tejada, P. (2015). High-Resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: Application in breeding trials. Remote Sensing, 7(4), 4213–4232. https://doi.org/10.3390/rs70404213Dong, J., Burnham, J.G., Boots, B., Rains, G., Dellaert, F. (2017). 4D crop monitoring: Spatio-temporal reconstruction for agriculture. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 3878–3885. https://doi.org/10.1109/ICRA.2017.7989447.Ehlert, D., Heisig, M., & Adamek, R. (2010). Suitability of a laser rangefinder to characterize winter wheat. Precision Agriculture, 11, 650–663.Escolà, A., Martínez-Casasnovas, J. A., Rufat, J., Arnó, J., Arbonés, A., Sebé, F., Pascual, M., Gregorio, E., & Rosell-Polo, J. R. (2017). Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 18, 111–132. https://doi.org/10.1007/s11119-016-9474-5Estornell, J., Ruiz, L. A., Velázquez-Martí, B., López-Cortés, I., Salazar, D., & Fernández-Sarría, A. (2015). Estimation of pruning biomass of olive trees using airborne discrete-return LiDAR data. Biomass and Bioenergy, 81, 315–321. https://doi.org/10.1016/j.biombioe.2015.07.015Estornell, J., Velázquez-Martí, A., Fernández-Sarría, A., López-Cortés, I., Martí-Gavilá, J., & Salazar, D. (2017). Estimation of structural attributes of walnut trees based on terrestrial laser scanning. Revista de Teledetección, 0(48), 67–76. https://doi.org/10.4995/raet.2017.7429Estornell, J., Velázquez-Martí, B., Fernández-Sarría, A., & Martí, J. (2018). Lidar methods for measurement of trees in urban forests. Journal of Applied Remote Sensing, 12(4), 046009. https://doi.org/10.1117/1.JRS.12.046009Fernández-Sarría, A., López-Cortés, I., Estornell, J., Velázquez-Martí, B., & Salazar, D. (2019). Estimating residual biomass of olive tree crops using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 75, 163–170. https://doi.org/10.1016/j.jag.2018.10.019Fernández-Sarría, A., Martínez, L., Velázquez-Martí, B., Sajdak, M., Estornell, J., & Recio, J. A. (2013). Different methodologies for calculating crown volumes of Platanus hispanica trees using terrestrial laser scanner and a comparison with classical dendrometric measurements. COMPAG, 90, 176–185.Gené-Mola, J., Gregorio López, E., Auat Cheein, F. A., Guevara, J., Llorens Calveras, J., Sanz Cortiella, R., & Rosell Polo, J. R. (2020). Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow. COMPAG, 168, 105121. https://doi.org/10.1016/j.compag.2019.105121Gil, E., Arnó, J., Llorens, J., Sanz, R., Llop, J., Rosell-Polo, J. R., Gallart, M., & Escolà, A. (2014). Advanced technologies for the improvement of spray application techniques in Spanish viticulture: An overview. Sensors, 14, 691–708. https://doi.org/10.3390/s140100691Goesele, M., Snavely, N., Seitz, S.M., Curless, B., Hoppe, H. (2007). Multi-view stereo for community photo collections. In Proceedings of the11th International Conference on Computer Vision, Rio de Janeiro, Brazil pp. 1–8. https://doi.org/10.1109/ICCV.2007.4408933.Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56. https://doi.org/10.1007/s11119-013-9335-4Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., Martius, C., Goodman, R., Disney, M., Manuri, S., et al. (2018). Estimation of above ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution, 9(2), 223–234. https://doi.org/10.1111/2041-210X.12904Guerra-Hernández, J., Cosenza, D. N., Estraviz Rodriguez, L. C., Silva, M., Tomé, M., Díaz-Varela, R. A., & González-Ferreiro, E. (2018). Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15–16), 5211–5235. https://doi.org/10.1080/01431161.2018.1486519Hadas, E., Borkowski, A., Estornell, J., & Tymkow, P. (2017). Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis. GIScience & Remote Sensing, 54(6), 898–917. https://doi.org/10.1080/15481603.2017.1351148Hargreaves, G. H. (1994). Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering. https://doi.org/10.1061/(ASCE)0733-9437(1994)120:6(1132)Heinzel, J., & Huber, M. O. (2017). Tree stem diameter estimation from volumetric TLS image data. Remote Sensing, 9, 614. https://doi.org/10.3390/rs9060614Jay, S., Rabatel, G., Hadoux, X., Moura, D., & Gorretta, N. (2015). In-field crop row phenotyping from 3D modeling performed using Structure from motion. COMPAG, 110, 70–77.Jenkins, J., Chojnack, D., Heath, L., Birdsey, R. (2004). Comprehensive database of diameter-based biomass regressions for north american tree Species; United States Forest Service, Northeastern Research Station, p. 45.Jiménez-Brenes, F. M., López-Granados, F., Castro, A. I., Torres-Sánchez, J., Serrano, N., & Peña, J. M. (2017). Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods, 13, 1–15. https://doi.org/10.1186/s13007-017-0205-3Kankare, I., Räty, M., Yu, X., Holopainen, M., Vastaranta, M., Kantola, T., Hyyppä, J., Hyyppä, H., Alho, P., & Viitala, R. (2013). Single tree biomass modelling using airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 66–73. https://doi.org/10.1016/j.isprsjprs.2013.08.008Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). LiDAR remote sensing for ecosystem studies. BioScience, 52, 19–30.Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakola, A., Guan, F., Holopainen, M., & Vastaranta, M. (2016). Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 63–77.López-Cortés, I., Martí-Gavilá, J., Estornell, J., Fernández-Sarría, A. (2019). Comparación de parámetros de olivos a partir de UAV y datos LiDAR aéreos. In Proceedings of XVIII Congreso de la Asociación Española de Teledetección, Valladolid, pp. 24–27 September 2019; pp. 439–442.Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94Maltamo, M., Räty, J., Korhonen, L., Kotivuori, E., Kukkonen, M., Peltola, H., Kangas, J., & Packalen, P. (2020). Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images. European Journal of Remote Sensing. https://doi.org/10.1080/22797254.2020.1816142Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10, 45–62. https://doi.org/10.1007/s11119-008-9091-zMiller, J., Morgenroth, J., & Gomez, C. (2015). 3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates. Urban for Urban Green, 14(4), 932–940. https://doi.org/10.1016/j.ufug.2015.09.001Ministerio de Agricultura, Pesca y Alimentación (2019). Encuesta sobre superficies y rendimientos cultivos (ESYRCE). Encuesta de marco de áreas de España. p. 178. Retrieved 15 Sept 2021 from https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/esyrce/.Miranda-Fuentes, A., Llorens, J., Rodriguez-Lizana, A., Cuenca, A., Gil, E., Blanco-Roldán, G. L., & Gil-Ribes, J. A. (2016). Assessing the optimal liquid volume to be sprayed on isolated olive trees according to their canopy volumes. STOTEN, 568, 296–305. https://doi.org/10.1016/j.scitotenv.2016.06.013Moorthy, I., Miller, J. R., Jimenez Berni, J. A., Zarco-Tejada, P., Hu, B., & Chen, J. (2011). Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2), 204–214. https://doi.org/10.1016/j.agrformet.2010.10.005Neuenschwander, A., & Pitts, K. (2019). The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221, 247–259. https://doi.org/10.1016/j.rse.2018.11.005Palacín, J., Pallejà, T., Tresanchez, M., Sanz, R., Llorens, J., Ribes-Dasi, M., Masip, J., Arnó, J., Escolà, A., & Rosell, J. R. (2007). Real-time tree-foliage surface estimation using a ground laser scanner. IEEE Transactions on Instrumentation and Measurement, 56(4), 1377–1383. https://doi.org/10.1109/TIM.2007.900126Pierzchala, M., Giguère, P., & Astrup, R. (2018). Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-slam. COMPAG, 145, 217–225. https://doi.org/10.1016/j.compag.2017.12.034Pitkänen, T. P., Raumonen, P., & Kangas, A. (2019). Measuring stem diameters with TLS in boreal forests complementary fitting procedure. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 294–306. https://doi.org/10.1016/j.isprsjprs.2018.11.027Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 31(9), 646–655. https://doi.org/10.1016/j.biombioe.2007.06.022Sanz, R., Llorens, J., Escolà, A., Arnó, J., Planas, S., Román, C., & Rosell-Polo, J. R. (2018). LiDAR and non-LiDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agricultural and Forest Meteorology, 260–261, 229–239. https://doi.org/10.1016/j.agrformet.2018.06.017Schlaegel, B. (1984). Green ash volume and weight tables. US Department of Agriculture, Forest Service, Southern Forest Experiment Station.Sheridan, R. D., Popescu, S. C., Gatziolis, D., Morgan, C. L. S., & Ku, N. W. (2015). Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest. Remote Sensing, 7, 229–255. https://doi.org/10.3390/rs70100229Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from Internet photo collections. International Journal of Computer Vision, 80(2), 189–210. https://doi.org/10.1007/s11263-007-0107-3Stereńczak, K., Kraszewski, B., Mielcarek, M., Piasecka, N., Lisiewicz, M., & Heurich, M. (2020). Mapping individual trees with airborne laser scanning data in an European lowland forest using a selfcalibration algorithm. International Journal of Applied Earth Observation and Geoinformation, 93, 102191. https://doi.org/10.1016/j.jag.2020.102191Tagarakis, A. C., Koundouras, S., Fountas, S., & Gemtos, T. (2018). Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation. Precision Agriculture, 19, 334–347. https://doi.org/10.1007/s11119-017-9519-4Torres-Sánchez, J., de Castro, A., Peña, J. M., Jiménez-Brenes, F. M., Arquero, O., Lovera, M., et al. (2018). Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis. Biosystems Engineering, 176, 172–184. https://doi.org/10.1016/j.biosystemseng.2018.10.018Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., & Peña, J. M. (2015). High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE, 10(6), e0130479. https://doi.org/10.1371/journal.pone.0130479Valbuena, M. A., Santamaría, J., & Sanz, F. (2016). Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the national forest inventory data. Forest Systems, 25(1), e046. https://doi.org/10.5424/fs/2016251-05790Van der Zande, V., Hoet, W., Jonckheere, I., van Aardt, J., & Coppin, P. (2006). Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agricultural and Forest Meteorology, 141(2–4), 147–160. https://doi.org/10.1016/j.agrformet.2006.09.007Velázquez-Martí, B., Fernández-González, E., López-Cortes, I., & Salazar-Hernández, D. M. (2011). Quantification of the residual biomass obtained from pruning of trees in Mediterranean olive groves. Biomass and Bioenergy, 35(2), 3208–3217. https://doi.org/10.1016/j.biombioe.2011.04.042Velázquez-Martí, B., López-Cortés, I., & Salazar, D. M. (2014). Dendrometric analysis of olive trees for wood biomass quantification in Mediterranean orchards. Agroforestry Systems, 88(5), 755–765. https://doi.org/10.1007/s10457-014-9718-1Walklate, P. J., Cross, J. V., Richardson, G. M., Murray, R. A., & Baker, D. E. (2002). Comparison of different spray volume deposition models using LIDAR measurements of apple orchards. Biosystems Engineering, 82(3), 253–267.Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., & Vopenka, P. (2016). Assessment of forest structure using two UAV Techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7, 62. https://doi.org/10.3390/f7030062Wang, X., Cheng, X., Gong, P., Huang, H., Li, Z., & Li, X. (2011). Earth science applications of ICESat/GLAS: A review. International Journal of Remote Sensing, 32(23), 8837–8864.Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer, N. (2017). A case study of UAS borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9, 1154. https://doi.org/10.3390/rs9111154Xie, Q., Dash, J., Huete, A., Jiang, A., Yin, G., Ding, Y., Peng, D., Hall, C. C., Brown, L., Shi, Y., et al. (2019). Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 80, 187–195. https://doi.org/10.1016/j.jag.2019.04.019Xing, D., Bergeron, J. C., Solarik, K. A., Tomm, B., Macdonald, S. E., Spence, J. R., & He, F. (2019). Challenges in estimating forest biomass: Use of allometric equations for three boreal tree species. Canadian Journal of Forest Research, 49(12), 1613–1622.Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V., & Loudjani, P. (2014). Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 255, 89–99.Zhang, Z., Cao, L., & She, G. (2017). Estimating forest structural parameters using canopy metrics derived from airborne LiDAR data in subtropical forests. Remote Sens, 9, 940. https://doi.org/10.3390/rs909094

A New Mechanism of Sodium Zirconate Formation

The objective of this study was to synthesize sodium zirconate (Na2ZrO3) from the thermal decomposition of two reactants; sodium acetate (CH3COONa) and Zirconium(IV) acetylacetonate (Zr(C5H7O2)4). The Na2ZrO3 formation mechanism has not been previously reported as it is shown in this work. Also, the reagents are soluble in ethanol; making it possible to apply the mechanism proposed in a spray pyrolysis process. The solid-state reaction was derived from the thermal decomposition of its precursors through the thermogravimetric analysis techniques (TG). The desired product formation was proven by means of an x-ray diffraction technique while the gaseous by-products of the reaction were analyzed using of the IR spectroscopy method (FTIR). Solid-state reaction has three significant weight losses and the TG technique displays these behaviors. The kinetic reaction study was completed using the determination of the activation energy, the pre-exponential factor and the reaction order of such regions. Finally, it was numerically proven that the chemical reaction behavior is well reproduced using the Arrhenius-type kinetic model. Keywords: Sodium zirconate, Arrhenius equation, solid-state reaction, thermal decomposition

Proposal of a New Orange Selection Process Using Sensory Panels and AHP

[EN] Although the consumption of fruits and vegetables is being promoted by different institutions as a key question of public health, their consumption is decreasing and their waste is increasing. To address this situation, we propose to include the consumer¿s perception of the quality (from a sensory point of view) of a fruit, in particular Valencian oranges, in the supplier¿s selection process by retailers. To do so, we use a combination of consumer and trained sensory panels and Analytic Hierarchy Process (AHP). This approach is completely novel in the literature. According to the expert panel, the most important criteria when evaluating the quality of an orange are fruity smell, juiciness, sweetness and acidity. These criteria are related to the freshness and taste of the oranges. Consumers found the methodology proposed useful and easy to develop. The application of the AHP methodology has helped to facilitate a participatory discussion among consumers on the concept of the quality of the oranges. The methodology proposed can help the agrifood sector in different ways up and down the supply chain. Specially, it can contribute to better meet consumer¿s demands, increasing the consumption of fruits and vegetables and reducing its waste.Baviera-Puig, A.; García-Melón, M.; Ortolá Ortolá, MD.; López- Cortés, I. (2021). Proposal of a New Orange Selection Process Using Sensory Panels and AHP. International Journal of Environmental research and Public Health (Online). 18(7):1-17. https://doi.org/10.3390/ijerph18073333S11718

Xylella fastidiosa (Wells&Raju). Flavescencia dorada. Enfermedad de Pierce

López- Cortés, I.; Salazar Hernández, DM. (2018). Xylella fastidiosa (Wells&Raju). Flavescencia dorada. Enfermedad de Pierce. La Semana Vitivinicola. (3514):324-328. http://hdl.handle.net/10251/124690S324328351

Xylella fastidiosa: amenaza real para el viñedo

Salazar Hernández, DM.; López- Cortés, I. (2018). Xylella fastidiosa: amenaza real para el viñedo. La Semana Vitivinicola. (3512):164-172. http://hdl.handle.net/10251/124677S164172351

The use of biostimulants in high-density olive growing: Quality and production

[EN] Due to the increase of high-density holdings, especially of olive trees, the nutritional requirements of the plants are higher per unit area, which implies that a greater contribution of fertilizers to the soil is needed. Opting for fertilizers of inorganic origin will produce an increase in the pollution of the soil. In the face of this possible soil contamination, our aim is to analyze the effect of biostimulants as an alternative to chemical fertilizers, to steadily produce and maintain high quality standards during the life of the crop. Our objective is using more environmentally friendly products in order to satisfy one of the most important demands from both consumers and the authorities. In this study, we carried out five different treatments in addition to a control treatment with a supply of NPK, from inorganic products, which are used to control fertilization with a solution obtained from seaweed extracts. These treatments were applied in two crop cycles for two of the most important varieties in the current olive tree growing scenario: Arbequina and Koroneiki. This study was developed in the farm Pozohondo, which is located in a crop zone by the Palancia river (Castellón, Valencia, Spain), in the southeast of the Iberian Peninsula, where the olive trees were established in a high-density system with a planting framework of 4 x 1.5 m. We ensured an exhaustive control of the nutritional needs of the holding by using a fertigation system. We could notice differences in the productions of each applied treatment, avoiding any possible biases through the additional control of 100 randomly selected olives from each of the samples. There is an improvement in the set of physical characteristics of the olives with the treatment that provides amino acids and extra potassium based on amni acids. We analyzed the quality of the olive oil obtained from the production of each treatment by measuring the fatty acids, tocopherols and polyphenols contents. We also carried out an organoleptic tasting analysis following the rules of the International Olive Committee (IOC). We observed an improvement with regard to the rest of treatments in the pomological parameters of the olives when applying the potassium and amino acid biostimulant, while the quality of the oils was not affected by the type of fertilization applied in each treatment.This work was funded by Project AICO/2017/047. Development of methods of quantification of riparian vegetation biomass for the management of channels of the Comunitat Valenciana. Dirección General de Universidades. Generalitat Valenciana (Spain).Hernández-Hernández, GJ.; Salazar Hernández, DM.; Martínez-Tomé, J.; López-Cortés, I. (2019). The use of biostimulants in high-density olive growing: Quality and production. Asian Journal of Advances in Agricultural Research. 10(4):1-11. https://doi.org/10.9734/AJAAR/2019/v10i430034S11110

Changes produced by the application of biostimulants on almond rootstocks properties during the nursery process

[EN] During the last ten years we have assisted to the consolidation of the almond crop that has remarkably increased its cultivation area causing a high demand for both plants and products related to growth stimulation. Accordingly, in the present work we aim to study the changes produced by the contribution of two biostimulants (humic and fulvic acids or aminoacids) on the properties of almond tree rootstocks. This kind of studies are of interest to the nursery cultivation industry where rapid growth of trees and good adaptation to their cultivation environment are required. Plants' radicular and vegetative systems responded differently according to the rootstock selection. The fastest and vigorous vegetative development was observed in GN rootstock whereas GF 677 showed the greatest number of main roots and RP-R of secondary roots. Differences on antioxidant activity and phenol content have also been found between rootstocks. All the tested samples were found to have a high antioxidant power and a high phenol content but GN stood out in this regard over the other rootstocks under study. The efficiency of the biostimulants applied has been verified. Both biostimulants promoted the development of the aerial part of the trees but biostimulant 2 did it to a greater extent. Biostimulant 1 was able to duplicate the number of main roots in RP-R and during the first year of study, biostimulant 2 originated an increase of the weight of the root system by 26.44% for RP-R, 16.93% for GF 677 and 48.00% for GN. In view of these results, synthetic chemical fertilizers can be at least partially replaced by biostimulants.Mondragón-Valero, A.; Malheiro, R.; Salazar Hernández, DM.; Pereira, JA.; López- Cortés, I. (2019). Changes produced by the application of biostimulants on almond rootstocks properties during the nursery process. Advances in Agriculture & Botanics (Online). 11(1):56-71. http://hdl.handle.net/10251/151095S567111